Backscattering UWB/UHF hybrid solutions for multi-reader multi-tag passive RFID systems

Alesii, Roberto; Marco, Piergiuseppe Di; Santucci, F.; Savazzi, Pietro; Valentini, Roberto; Vizziello, Anna

doi:10.1186/s13639-016-0031-0

Cited by 7 publications

(2 citation statements)

References 26 publications

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“…RFID is a non-contact and non-line-of-sight automatic identification composed of electronic tags, card readers, and advanced structure. It can identify target objects through radio frequency signals and obtain relevant data information [32]. Its basic structure is shown in Figure 3.…”

Section: Rfid Of Iotmentioning

confidence: 99%

Tennis Robot Design via Internet of Things and Deep Learning

Wang

2021

IEEE Access

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To efficiently integrate deep learning (DL) and Internet of Things (IoT) with tennis robot research, the concept and characteristics of mask region-convolutional neural network (Mask R-CNN) under DL are analyzed. Then, the IoT radio frequency identification (RFID) is introduced and the suitable RFID structure is established. Moreover, the real-time intelligent image recognition tennis robot is designed, and simulation experiment on the robot is performed. The results show that when the positioning label is eight, the convergence speed of the optimized algorithm is improved relative to the unoptimized one, and the error is reduced by 0.82. The positioning algorithm proposed in this research has high convergence speed and small system error. The positioning accuracy of the tennis robot is improved by at least 6%, the positioning targets are close to the target to be located, and the tennis robot has the best shortest path effect. In addition, the tennis recognition algorithm based on Mask R-CNN can accurately distinguish dense tennis balls with high accuracy. It shows that the tennis robot positioning algorithm and target recognition algorithm proposed in this research are of practical adoption value.

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Section: Rfid Of Iotmentioning

confidence: 99%

Tennis Robot Design via Internet of Things and Deep Learning

Wang

2021

IEEE Access

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“…In order to reduce the cost, these systems use low-frequency (LF) RFID devices and a low-cost, but whereas typical race systems use ultra-high frequency (UHF) RFID readers, in which the range is 5–30 m, LF RFID readers present a range of only 10–20 cm [ 6 ]. Other solutions propose an identification mechanism that uses Ultra Wide Band (UWB) ranging information to improve the speed of identification [ 7 ], but increasing the complexity and the cost of the system. In any case, RFID technology, as most of radiofrequency technologies, does not intrinsically provide aptitudes for high spatial resolution and to identify the time of presence of the tag on a specific place with high precision, particularly in complex propagation environments.…”

Section: Introductionmentioning

confidence: 99%

A Low-Cost Tracking System for Running Race Applications Based on Bluetooth Low Energy Technology

Perez-Diaz-de-Cerio

Hernández-Solana

Valdovinos

et al. 2018

Sensors

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Timing points used in running races and other competition events are generally based on radio-frequency identification (RFID) technology. Athletes’ times are calculated via passive RFID tags and reader kits. Specifically, the reader infrastructure needed is complex and requires the deployment of a mat or ramps which hide the receiver antennae under them. Moreover, with the employed tags, it is not possible to transmit additional and dynamic information such as pulse or oximetry monitoring, alarms, etc. In this paper we present a system based on two low complex schemes allowed in Bluetooth Low Energy (BLE): the non-connectable undirected advertisement process and a modified version of scannable undirected advertisement process using the new capabilities present in Bluetooth 5. After fully describing the system architecture, which allows full real-time position monitoring of the runners using mobile phones on the organizer side and BLE sensors on the participants’ side, we derive the mobility patterns of runners and capacity requirements, which are determinant for evaluating the performance of the proposed system. They have been obtained from the analysis of the real data measured in the last Barcelona Marathon. By means of simulations, we demonstrate that, even under disadvantageous conditions (50% error ratio), both schemes perform reliably and are able to detect the 100% of the participants in all the cases. The cell coverage of the system needs to be adjusted when non-connectable process is considered. Nevertheless, through simulation and experimental, we show that the proposed scheme based on the new events available in Bluetooth 5 is clearly the best implementation alternative for all the cases, no matter the coverage area and the runner speed. The proposal widely exceeds the detection requirements of the real scenario, surpassing the measured peaks of 20 sensors per second incoming in the coverage area, moving at speeds that range from 1.5 m/s to 6.25 m/s. The designed real test-bed shows that the scheme is able to detect 72 sensors below 600 ms, fulfilling comfortably the requirements determined for the intended application. The main disadvantage of this system would be that the sensors are active, but we have proved that its consumption can be so low (9.5 µA) that, with a typical button cell, the sensor battery life would be over 10,000 h of use.

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Link Budget Analysis for Backscatter-Based Passive IoT

et al. 2022

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Massive connectivity of billions of communicating devices for fifth-generation (5G) and beyond networks requires the deployment of self-sustaining, maintenance-free, and low-cost communication paradigms. Could passive Internet of Things (IoT) solve these challenges? Passive IoT can be realized with the backscatter communication (BackCom) paradigm, which uses ultra-low power, inexpensive passive tags to support massive connectivity. However, a comprehensive link budget analysis for BackCom networks has not yet been available. It is something that is necessary for practitioners and researchers to evaluate the potential of BackCom. This survey is organized as follows. First, we describe the BackCom configurations, passive IoT design targets, backscatter channel statistics, and the different components and operations of the backscatter tag. Second, we develop the forward link budget and the overall link budget. All the relevant parameters are described in detail. Third, we give numerical and simulation results to get insights on the achievable performance of BackCom networks. Since additive path losses and excess fading can limit the performance of BackCom networks, we examine potential solutions to overcome the resulting limitations, enabling massive IoT networks. We also discuss integrating BackCom with existing wireless technologies. We further highlight some applications and address open issues, challenges, and future research directions.

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Backscattering UWB/UHF hybrid solutions for multi-reader multi-tag passive RFID systems

Cited by 7 publications

References 26 publications

Tennis Robot Design via Internet of Things and Deep Learning

Tennis Robot Design via Internet of Things and Deep Learning

A Low-Cost Tracking System for Running Race Applications Based on Bluetooth Low Energy Technology

Link Budget Analysis for Backscatter-Based Passive IoT

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